Biologically Inspired Self-Organizing Map Applied to Task Assignment and Path Planning of an AUV System

• Published in: IEEE transactions on cognitive and developmental systems Vol. 10; no. 2; pp. 304 - 313
• Main Authors: Zhu, Daqi, Cao, Xiang, Sun, Bing, Luo, Chaomin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous underwater vehicles; Autonomous underwater vehicles (AUVs); Biological neural networks; biologically inspired self-organizing map (BISOM); Computer simulation; Embedded systems; Heuristic algorithms; Neural networks; Neurons; Obstacle avoidance; Path planning; Product design; Self organizing maps; Self-organizing feature maps; speed jump; task assignment and path planning; Three-dimensional displays
• ISSN: 2379-8920; 2379-8939
• DOI: 10.1109/TCDS.2017.2727678

An integrated biologically inspired self-organizing map (SOM) algorithm is proposed for task assignment and path planning of an autonomous underwater vehicle (AUV) system in 3-D underwater environments with obstacle avoidance. The algorithm embeds the biologically inspired neural network (BINN) into the SOM neural networks. The task assignment and path planning aim to arrange a team of AUVs to visit all appointed target locations, while assuring obstacle avoidance without speed jump. The SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in underwater environments. Then, in order to avoid obstacles and speed jump for each AUV that visits the corresponding target location, the BINN is utilized to update weights of the winner of SOM, and achieve AUVs path planning and effective navigation. The effectiveness of the proposed hybrid model is validated by simulation studies.